[1]} argue that combining POL and NLG is critical to make the approach scalable.
By separating POL and NLG, the system actions need to be labeled for training.
However, system actions are task-specific labels, and are not easy to be collected in large amounts.
The only labels needed for training GTG, on the other hand, are belief states, which are not only relatively task-independent but similar to named entity annotations that can be collected in large quantities much more easily, e.g., by transforming Wikipedia documents [2]}.
